Multiple intelligence (Multi-INT) fusion refers to the fusion of data from multiple sources into one relatively complete picture, which helps to detect, locate, and track objects or activities. Major types of data includes unstructured data coming from open sources like Tweets, and structured data collected by sensors such as a video camera, radar, and infrared sensor.
Pattern discovery and anomaly detection are critical processes in Multi-INT fusion for situation assessment and surveillance. To extract patterns from the structured data, conventional machine learning algorithms or data mining techniques may be applied. For example, a conventional machine learning algorithm may use the structured data to learn the mapping from sensor input to output patterns. Based on the mapping, abnormal patterns may be identified, so as to identity abnormal activities.
However, for unstructured data received from open sources, such as real-time tweets, it is often difficult to apply the conventional learning algorithm to extract patterns and to further identify abnormal activities based on the extracted patterns. Thus, a method and a system for extracting patterns from unstructured data is desired.
The disclosed method and system for pattern discovery and real-time anomaly detection based on knowledge graph are directed to solving one or more problems set forth above and other problems in the art.